Investigating the Effectiveness of Emotion-Focused Therapy on Perceived Stress and Alexithymia in Patients With Coronary Heart Disease in Rasht City, Iran
Bibliographic record
Abstract
Background Coronary heart disease is one of the most common diseases that can affect the mental and bodily health of people. Objective This study aims to evaluate the effectiveness of emotion-focused therapy (EFT) on perceived stress and alexithymia in patients with coronary heart disease. Methods This was a quasi-experimental study with a pre-test-post-test design with a control group in 2019 on 30 patients diagnosed with coronary heart disease who were referred to Dr. Heshmat Hospital in Rasht City, Iran. The patients were selected by simple random sampling and assigned to 2 groups of 15, including an experimental group and a control group. The assessment tools included the 14-item perceived stress scale (PSS-14) and the 20-item Toronto alexithymia scale (TAS-20), which were completed before and after the intervention. The experimental group received EFT in eight 90-min sessions for two months. The data were analyzed using descriptive statistics, multivariate analysis of covariance and the Bonferroni post hoc test. Results There was a significant difference between the experimental group that underwent EFT and the control group that did not receive any treatment (F2, 25= 170.5, P<0.001, η2P=0.932). Meanwhile, according to the adjusted means, this difference indicates the effectiveness of EFT. Conclusion EFT training can play an important role as an adjunctive and psychological treatment along with drug intervention in reducing the perceived stress and alexithymia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".